AI governance platforms are becoming essential infrastructure for organizations deploying AI responsibly. These platforms provide the tools and guardrails needed to define, monitor, and enforce governance policies across the AI lifecycle.
At the core of these platforms is metadata. Without rich, connected metadata, it’s nearly impossible to monitor AI systems effectively or answer basic governance questions like: What data was this model trained on? Who owns it? How is it performing over time?
This article will take you through:
- The definition, features, capabilities of AI governance platforms
- The need for platforms that enable you to enforce AI governance policies and procedures
- Why metadata is at the centre of the world of AI governance
- How to use metadata to drive AI governance
Table of Contents #
- What are AI governance platforms?
- Why do we need AI governance platforms?
- What are the key features of an AI governance platform?
- What is the importance of metadata in AI governance?
- How does a metadata control plane enable AI governance?
- Final thoughts on AI governance platforms
- AI governance platforms: Frequently asked questions (FAQs)
What are AI governance platforms? #
Gartner defines AI governance platforms as “technology solutions that enable organizations to manage and oversee the legal, ethical and operational performance of AI systems.”
In other words, AI governance platforms give you visibility into the use and abuse of AI systems, especially when it comes to areas such as bias detection, data privacy, intellectual property, and cybersecurity, among other things.
An AI governance platform should help your organization with the following:
- Aligning with governance and risk frameworks and policies
- Mitigating risks related to AI safety and responsibility
- Offering complete AI lifecycle visibility for transparency
- Building awareness about the risks of AI
- Preventing and detecting shadow AI use in the organization
- Reporting non-compliance with any binding laws or regulations
- Building a culture of accountability using granular controls
Different organizations, governments, and think tanks have their own interpretations on what AI governance should look like.
What makes sense for your organization will be based on the laws and regulations you’re bound by.
If you’re in the US, the AI Risk Management Framework created by NIST is a good place to start. If you’re in the EU, the EU AI Act is already in its first phase of implementation and you can use this as a roadmap.
Why do AI governance platforms matter in 2025? #
There is always a lag between a new computer technology coming into existence and laws and regulations being enforced around its use. In the interim, companies and academia develop early frameworks to mitigate any risk that the technology might pose. That’s exactly what has happened with AI, especially with the breakout of generative AI, where innovation has outpaced regulation.
AI governance helps navigate these uncharted waters. It is the collection of activities organizations must undertake to comply with laws and regulations by setting up AI usage and deployment policies and procedures. Moreover, ethical considerations need to be taken care of, too, especially in specific industries like medical sciences, surveillance, consumer finance, etc.
But policies alone aren’t enough. Governance requires operational infrastructure. This is where AI governance platforms come in. These platforms help teams:
- Define and enforce AI usage policies
- Track model performance and risk over time
- Monitor compliance, auditability and ethical safeguards across AI workflows
Next, let’s take a closer look at how these needs translate into actual platform features.
What are the key features of an AI governance platform? #
To meet compliance obligations and manage risk, AI governance platforms need features that adapt to specific industries, geographies, and use cases.
Whether it’s bias detection in financial or medical models, explainability in product recommendations, or audit trails for regulatory reporting, your AI governance platform must be equipped to handle these challenges.
To do that, an AI governance platform needs the following features:
- Extensive auditing of model deployment, access, and usage
- Policy enforcement for data and AI usage
- Centralized access controls for detecting and preventing shadow AI
- End-to-end data lineage tracking for transparency and compliance
- Collaboration features to align everyone within the organization
- Semantic data catalog for easy access and use
When these features are well-implemented, they give governance stakeholders the tools to monitor, mitigate, and report AI-related risks—internally and externally.
You’ll notice that the features of an AI governance platform are somewhat similar to those of a data governance platform. That’s because they both have a foundation of metadata, upon which they’re built.
Let’s look at why metadata is so crucial for AI governance.
Why is metadata critical for AI governance? #
Metadata is an umbrella term for all the data about data, and serves as the backbone of AI governance.
It includes everything from the structure of AI assets and ML pipeline definitions to model weights, access logs, and the lineage of data as it flows and transforms through systems.
All of this collected metadata lays the foundation for critical governance needs like observability, performance monitoring, access control, and lineage tracking. It lets you trace how data was used, by whom, and how it evolved, from its inception to its consumption, thereby ensuring transparency from model training to decision-making.
Using all this generated and collected metadata in a manual, non-standard way is not only cumbersome but can also lead to several of the issues that your organization wants to prevent.
An AI governance platform leverages this metadata in a structured, automated way to power the features discussed in the previous section—from lineage to policy enforcement.
Atlan is one such platform that turns metadata into a control layer for AI governance. Let’s take a closer look at how it works.
How does a metadata control plane enable AI governance? #
Atlan’s metadata control plane acts as a horizontal layer across all your data systems, integrations, and platforms, whether internal or external. It powers core capabilities like cataloging, lineage, policy enforcement, and monitoring, which are essential for governing AI systems at scale.
Here’s how Atlan supports AI governance through its metadata foundation:
- Data access control for extensive auditability, usage tracking, and traceability to understand who has access to the data and who is using it.
- Data lineage for tracing the change in the form and structure of the data from origin to destination, or from generation to consumption.
- Data asset 360° for having a complete view of any given data set, including basic data profiles and quality statistics, among other things.
- Unified Policy Center for creating and applying policies, regulatory, and compliance requirements to the data.
- Automation and AI for ensuring the handling of data doesn’t have any undue human interventions, and how metadata is used is through automation.
- Embedded collaboration for seamless integration with other tools in your workflow.
These capabilities make Atlan a strong choice for AI governance. Atlan was named a Visionary in the Gartner Magic Quadrant for Data and Analytics Governance Platforms and a Leader in the Forrester Wave: Enterprise Data Catalogs.
To learn more, visit Atlan’s official documentation.
Final thoughts on AI governance platforms #
AI governance platforms have become essential for managing risk, ensuring compliance, and enabling responsible AI deployment. Prominent features include data lineage, policy enforcement, collaboration, and visibility, among others.
These features are all grounded in rich, connected metadata. A metadata control plane brings these elements together, offering a unified layer to manage, track, and govern AI systems across their lifecycle.
To explore how this works in practice, visit Atlan’s official documentation.
AI governance platforms: Frequently asked questions (FAQs) #
1. What is an AI governance platform? #
An AI governance platform helps organizations oversee the legal, ethical, and operational performance of AI systems. It supports policy enforcement, risk monitoring, access control, and regulatory compliance across the AI lifecycle.
2. Why do organizations need AI governance platforms? #
As AI adoption grows, so do the risks around fairness, bias, security, and regulatory violations. AI governance platforms provide the infrastructure to monitor AI usage, enforce internal policies, and comply with external regulations like the EU AI Act or NIST AI RMF.
3. What are the key features of an AI governance platform? #
Core features include model and data lineage, access controls, policy management, audit trails, AI asset discovery, and collaboration tools. These capabilities ensure visibility, accountability, and traceability across AI systems.
4. How does metadata support AI governance? #
Metadata—data about your data and models—helps track the origin, transformations, access patterns, and use of AI assets. Rich metadata enables auditability, explainability, and compliance, making it the foundation of any AI governance platform.
5. How is AI governance different from data governance? #
While both rely on metadata, data governance focuses on managing data assets, whereas AI governance extends to models, pipelines, and algorithmic decision-making. It introduces policies for fairness, risk, and responsible AI use beyond raw data.
6. Can AI governance platforms prevent shadow AI? #
Yes. By centralizing access control and monitoring model deployment across the enterprise, AI governance platforms can detect and prevent unauthorized or unmonitored AI usage, often referred to as shadow AI.
7. What’s an example of an AI governance standard or regulation? #
Two major frameworks include the NIST AI Risk Management Framework (USA) and the EU AI Act. Both offer guidance on managing AI-related risks and ensuring ethical deployment.
8. What is the best AI governance platform? #
Atlan is one of the leading AI governance platforms available today. Built on active metadata, Atlan enables organizations to govern AI systems with capabilities like policy enforcement, model lineage, auditability, and access controls. It also supports integrations with the modern data stack and provides a unified metadata control plane, making it easier to operationalize responsible AI practices.